Object tracking systems are currently known and used. A limitation with current systems is the processing of information in a timely manner. Generally speaking, object tracking systems use a sensor such as radar or lidar to acquire data about an object that is detected within a predetermined range. The information is processed to calculate velocity vectors for each object so as to predict the location of the object in a predetermined time interval. The systems take another set of data so as to locate objects and then run an optimization filter to determine if the detected objects are associated with any one of the previously detected objects. Thus the system tracks objects in the predetermined area.
The information gathered by these sensors may be run through optimization filters which generate matrices associating each object with a previously detected object based upon factors such as the predicted location of the previously detected object, the color of the previously detected object, the height of the previously detected object, and the like. However, processing such matrices may be time consuming and inhibit the timeliness and effectiveness of current systems. Current platforms overcome these limitations by implementing hardware having robust processing speed and memory. However, such a countermeasure adds costs to implementation.
Accordingly it remains desirable to have an object tracking system which overcomes the limitations of the current art without having to increase the robustness of the hardware for processing such information.